Can NVIDIA GeForce RTX 4060 Ti 8GB run scGPT?
50M parameter Scientific Computing model on 8GB GDDR6
VRAM Requirements
scGPT is a 50M parameter model. At full precision (FP16), it requires 12GB of VRAM. Your NVIDIA GeForce RTX 4060 Ti 8GB has 8GB, so you'll need to quantize it to 8-bit (Q8) to fit.
Maximum quality, no quantization
Near-lossless, ~50% size reduction
Good quality, ~75% size reduction
Recommended system RAM: 32GB DDR5 (2x GPU VRAM minimum for model overflow)
What This Means in Practice
scGPT on NVIDIA GeForce RTX 4060 Ti 8GB handles medium single-cell datasets (10K-50K cells) well. Fine-tuning and inference for cell type annotation and gene network analysis run smoothly. For very large datasets (100K+ cells), consider batching or a GPU with more VRAM.
How to Set It Up
Step 1: Set up Python environment
conda create -n scicomp python=3.10 && conda activate scicompA clean Conda environment avoids dependency conflicts. Python 3.10 is recommended for most scientific computing tools.
Step 2: Install scGPT
pip install scgptFoundation model for single-cell RNA-seq. Requires scanpy and anndata for data handling.
Step 3: Load pre-trained model
Download pre-trained checkpoints from the scGPT GitHub repository. Fine-tune on your dataset for cell type annotation, perturbation prediction, or batch integration.
Step 4: Verify GPU is being used
nvidia-smiCheck that VRAM usage increases when the model loads. You should see ~8GB used.
NVIDIA GeForce RTX 4060 Ti 8GB Specs
Other GPUs That Run scGPT
Other Scientific Computing Models on NVIDIA GeForce RTX 4060 Ti 8GB
About scGPT
Foundation model for single-cell RNA-seq analysis. Fine-tune for cell type annotation, gene perturbation prediction, multi-batch integration, and multi-omics analysis. VRAM scales with dataset size — 100K+ cells need 12GB, smaller datasets fit on 4-8GB. Replaces traditional pipelines like Scanpy for many tasks.